Search Query Embeddings for Personalized Asset Relevance
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Solution Overview
Problem
Existing search algorithms face challenges in providing relevant results due to ambiguous queries, vast data volumes, language and cultural nuances, and personalization complexities, which affect the accuracy and privacy of search outcomes.
Innovation Solution
Implementing a method on client computing devices to generate digital asset embeddings using available features at intake, leveraging metadata, text, and attachments, and utilizing AI models for similarity analysis to provide personalized and relevant search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If search algorithms crawl and index immense numbers of web pages to access vast information, then the comprehensiveness of search results is improved, but the resource consumption and complexity of maintaining indexes increases
Solution Approach 1:
The system pre-generates embeddings for digital assets when they are added to the collection, rather than processing them at search time. This preliminary action stores the computational work in advance, allowing fast similarity searches without requiring complex real-time processing of vast data volumes.
Solution Approach 2:
The system creates vector embeddings as simplified numerical representations (copies) of the original digital assets. These embedding vectors capture the essential semantic information in a compact form that enables efficient comparison and search without requiring access to the full original content.
2Measurement precision
If search algorithms use user's search history for personalization, then the relevance of search results to individual users is improved, but the privacy risks and complexity of balancing personalization with privacy increases
Solution Approach 1:
The system uses vector embeddings as an intermediary representation that captures user preferences and asset characteristics in a mathematical space. This intermediary allows personalization through mathematical operations on embeddings without requiring direct access to or storage of sensitive user data, thereby reducing privacy risks while maintaining relevance.
3Adaptability or versatility
If search algorithms interpret ambiguous search queries, then the ability to understand user intent is improved, but the accuracy of interpretation decreases
Solution Approach 1:
The system transforms the search problem from text matching to vector space operations. By converting both queries and assets into embedding vectors, the system changes the parameter space from discrete text tokens to continuous numerical representations, enabling more nuanced interpretation of ambiguous queries through geometric relationships in the vector space.
Data Source
AI summary
One embodiment sets forth a method for providing relevant search results for search queries. According to some embodiments, the method can be implemented by a client computing device, and includes the steps of (1) receiving a query, wherein the query is associated with a user account, and the user account is associated with a user account vector, (2) generating a query vector based at least in part on the query, (3) generating an output vector based at least in part on the query vector and the user account vector, (4) obtaining, based at least in part on the query, a plurality of digital asset vectors, wherein each digital asset vector of the plurality of digital asset vectors corresponds to a respective digital asset, (5) comparing the output vector to the plurality of digital asset vectors to generate respective similarity scores for the plurality of digital asset vectors, (6) filtering the plurality of digital asset vectors in accordance with the similarity scores to establish a filtered plurality of digital asset vectors, and (7) displaying, in accordance with the filtered plurality of digital asset vectors, respective affordances for the respective digital assets that correspond to the filtered plurality of digital asset vectors.